Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI

Glioblastoma is a highly malignant tumor which mostly recurs locally around the resected contrast enhancement. However, it is difficult to identify tumor invasiveness pre-surgically, especially in non-enhancing areas. Thus, the aim of this thesis was to utilize multimodal MR technique to identify and characterize the peritumoral progression zone that eventually leads to tumor progression. Patients with newly diagnosed cerebral glioblastoma were included consecutively from our cohort between 2010 and2014. The presurgical MRI sequences included volumetric T1-weighted with contrast, FLAIR, T2-weighted, diffusion-weighted imaging, diffusion tensor and perfusion MR imaging. Postsurgical and follow-up MRI included structural and ADC images. Image deformation, caused by disease nature and surgical procedure, renders routine coregistration methods inadequate for MRIs comparison between different time points. Therefore, a two-staged non-linear semi-automatic coregistration method was developed from the modification of the linear FLIRT and non-linear FNIRT functions in FMRIB’s Software Library (FSL). Utilising the above mentioned coregistration method, a volumetric study was conducted to analyse the extent of resection based on different MR techniques, including T1 weighted with contrast, FLAIR and DTI measures of isotropy (DTI-p) and anisotropy (DTI-q). The results showed that patients can have a better clinical outcome with a larger resection of the abnormal DTI q areas. Further study of the imaging characteristics of abnormal peritumoural DTI-q areas, using MRS and DCS-MRI, showed a higher Choline/NAA ratio (p = 0.035), especially higher Choline (p = 0.022), in these areas when compared to normal DTI-q areas. This was indicative of tumour activity in the peritumoural abnormal DTI-q areas. The peritumoural progression areas were found to have distinct imaging characteristics. In these progression areas, compared to non-progression areas within a 10 mm border around the contrast enhancing lesion, there was higher signal intensity in FLAIR (p = 0.02), and T1C (p < 0.001), and there were lower intensity in ADC (p = 0.029) and DTI-p (p < 0.001). Further applying radiomics features showed that 35 first order features and 77 second order features were significantly different between progression and non-progression areas. By using supervised convolutional neural network, there was an overall accuracy of 92.4% in the training set (n = 37) and 78.5% in the validation set (n=14). In summary, multimodal MR imaging, particularly diffusion tensor imaging, can demonstrate distinct characteristics in areas of potential progression on preoperative MRI, which can be considered potential targets for treatment. Further application of radiomics and machine learning can be potentially useful when identifying the tumor invasive margin before the surgery.Chung Gung Medical Foundatio

Bayesian generative learning of brain and spinal cord templates from neuroimaging datasets

In the field of neuroimaging, Bayesian modelling techniques have been largely adopted and recognised as powerful tools for the purpose of extracting quantitative anatomical and functional information from medical scans. Nevertheless the potential of Bayesian inference has not yet been fully exploited, as many available tools rely on point estimation techniques, such as maximum likelihood estimation, rather than on full Bayesian inference. The aim of this thesis is to explore the value of approximate learning schemes, for instance variational Bayes, to perform inference from brain and spinal cord MRI data. The applications that will be explored in this work mainly concern image segmentation and atlas construction, with a particular emphasis on the problem of shape and intensity prior learning, from large training data sets of structural MR scans. The resulting computational tools are intended to enable integrated brain and spinal cord morphometric analyses, as opposed to the approach that is most commonly adopted in neuroimaging, which consists in optimising separate tools for brain and spine morphometrics

Registration of histology and magnetic resonance imaging of the brain

Combining histology and non-invasive imaging has been attracting the attention of the medical imaging community for a long time, due to its potential to correlate macroscopic information with the underlying microscopic properties of tissues. Histology is an invasive procedure that disrupts the spatial arrangement of the tissue components but enables visualisation and characterisation at a cellular level. In contrast, macroscopic imaging allows non-invasive acquisition of volumetric information but does not provide any microscopic details. Through the establishment of spatial correspondences obtained via image registration, it is possible to compare micro- and macroscopic information and to recover the original histological arrangement in three dimensions. In this thesis, I present: (i) a survey of the literature relative to methods for histology reconstruction with and without the help of 3D medical imaging; (ii) a graph-theoretic method for histology volume reconstruction from sets of 2D sections, without external information; (iii) a method for multimodal 2D linear registration between histology and MRI based on partial matching of shape-informative boundaries

Sporadic cerebral small vessel disease and cognitive abilities

Cerebral small vessel disease (SVD) is a leading cause of vascular cognitive impairment, contributing to multiple neurological disorders ranging from stroke, to mild cognitive impairment and dementia. However, despite a huge number of studies on the subject, we have a limited understanding of how SVD affects cognitive ability. This thesis aims to address this knowledge gap, by examining domain-specific cognitive abilities in a range of clinical and non-clinical presentations of SVD. In the introductory chapters of this thesis I will discuss what is meant by the term cerebral small vessel disease (SVD), describing key radiological features of SVD and its varied clinical and non-clinical presentations. However, before considering the current consensus on how SVD impacts different domains of cognitive ability, I will first consider what happens to these abilities in the context healthy cognitive ageing. Finally, I will consider the current consensus on the pattern of cognitive changes that occur in SVD and will examine the vast and often conflicting evidence that underpins this. To gain a comprehensive overview of the published literature examining cognitive abilities in SVD, Chapter 4 presents a systematic review and meta-analysis of 69 studies presenting cognitive data for at least one cohort with SVD (n=3679) and one comparison control group without SVD (n=3229). Results indicated that relative to controls, cohorts with SVD performed more poorly on cognitive tests in all of the cognitive domains examined. Meta-regression analyses suggested that fewer years of education in the SVD vs. control groups accounted for a proportion of the differences in their test scores in some cognitive domains. Further meta-regression analyses suggested that cohorts with SVD-related cognitive impairment or dementia performed more poorly on tests in certain cognitive domains than cohorts with stroke or non-clinical presentations of SVD. Overall, however, SVD cohorts performed more poorly than controls on cognitive tests in all domains, regardless of their SVD presentation. Chapters 5 and 6 focus more closely on the key radiological markers of SVD and their associations with cognitive test scores using data from the Lothian Birth Cohort 1936 (LBC1936): a cohort of relatively healthy, community-dwelling, older individuals. To increase the fidelity with which SVD is typically measured, I combined computational volumes and visually-rated MRI markers of SVD to construct a variable representing the total MRI-visible burden of SVD. The study in Chapter 5 presents the results of cross-sectional associations between this latent SVD variable and latent variables of processing speed, verbal memory and visuospatial ability, within a structural equation modelling framework (SEM; n=540; mean age 72.6±0.7 years). Age, sex, vascular risk, depression status, and age-11 IQ were included as covariates. The latent SVD variable was negatively associated with all cognitive factors, in line with the results of the systematic review and meta-analysis. However, after accounting for the shared variance between the different cognitive domains (a construct described as general cognitive ability, which previous studies have not accounted for), only the association between the latent SVD variable and processing speed remained significant. This suggests that SVD’s association with slowed processing speed is not driven by, but is independent of its association with poorer general cognitive ability. In Chapter 6 this work is developed further by exploring associations between the latent SVD variable and decline in the same latent cognitive factors over a period of 9 years, from the age of around 73 to 82, again in the LBC1936. This was carried out using latent growth curve modelling within a SEM framework. Age, sex, vascular risk, and age-11 IQ were included as covariates. Results indicated that the latent SVD variable was associated with greater decline in general cognitive ability and processing speed. However, after accounting for the covariance between tests of processing speed and general cognitive ability, only the association between greater SVD burden and decline in general cognitive ability remained significant. Whereas the results of Chapter 5 suggested that SVD burden at age 73 may have specific and independent effects on processing speed measured at the same age, the results of our longitudinal analyses suggest that SVD burden at age 73 associates with declining processing speed due to SVD’s overarching association with general cognitive decline. In the final chapter of this thesis, I summarise the findings of these three studies, discuss their limitations, and make recommendations for future research

[¹⁸F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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GSI Scientific Report 2009 [GSI Report 2010-1]

Displacement design response spectrum is an essential component for the currently-developing displacement-based seismic design and assessment procedures. This paper proposes a new and simple method for constructing displacement design response spectra on soft soil sites. The method takes into account modifications of the seismic waves by the soil layers, giving due considerations to factors such as the level of bedrock shaking, material non-linearity, seismic impedance contrast at the interface between soil and bedrock, and plasticity of the soil layers. The model is particularly suited to applications in regions with a paucity of recorded strong ground motion data, from which empirical models cannot be reliably developed